#诊断逻辑代码

importsysimportosimporttimeimportoperatorimportcx_Oracleimportnumpy as npimportpandas as pdimporttensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor=conn.cursor()#one-hot编码

defonehot(labels):

n_sample=len(labels)

n_class= max(labels) + 1onehot_labels=np.zeros((n_sample, n_class))

onehot_labels[np.arange(n_sample), labels]= 1

returnonehot_labels#获取数据集

defgetdata(surgery ,surgeryChest):

sql= "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" %\

(surgery, surgeryChest)

cursor.execute(sql)

rows=cursor.fetchall()

dataset=[]

lables=[]for row inrows:

temp=[]

temp.append(row[0])

temp.append(row[1])

temp.append(row[2])

temp.append(row[3])

temp.append(row[4])

dataset.append(temp)if (row[5] == 3):

lables.append(0)elif (row[5] == 6):

lables.append(1)else:

lables.append(2)

dataset=np.array(dataset)

lables=np.array(lables)

dataset=dataset.astype(np.float32)

labless=onehot(lables)returndataset, lablessdefgetAnswer(a1, a2, a3, a4, a5):

answers=[]

answers.append(int(a1))

answers.append(int(a2))

answers.append(int(a3))

answers.append(int(a4))

answers.append(int(a5))

str_answers=[]if (int(a1) == 1):

str_answers.append("正常")elif (int(a1) == 2):

str_answers.append("轻度")elif (int(a1) == 3):

str_answers.append("偏重")else:

str_answers.append("严重")if (int(a2) == 1):

str_answers.append("正常")elif (int(a2) == 2):

str_answers.append("轻度")elif (int(a2) == 3):

str_answers.append("偏重")else:

str_answers.append("严重")if (int(a3) == 1):

str_answers.append("正常")elif (int(a3) == 2):

str_answers.append("轻度")elif (int(a3) == 3):

str_answers.append("偏重")else:

str_answers.append("严重")if (int(a4) == 1):

str_answers.append("正常")elif (int(a4) == 2):

str_answers.append("轻度")elif (int(a4) == 3):

str_answers.append("偏重")else:

str_answers.append("严重")if (int(a5) == 1):

str_answers.append("正常")elif (int(a5) == 2):

str_answers.append("轻度")elif (int(a5) == 3):

str_answers.append("偏重")else:

str_answers.append("严重")returnanswers, str_answersdefgetSugessiones(sujects, resultName):

sql= "select prescription_1,prescription_2,prescription_3,prescription_4,prescription_5,prescription_6,prescription_7,prescription_8,prescription_9,prescription_10,prescription_11,prescription_12,prescription_13,prescription_14,prescription_15 from prescription where SURGERYCHEST='%s' and illName='%s'" %(

sujects, resultName)

cursor.execute(sql)

rows=cursor.fetchall()

prescriptionData=[]for row inrows:

one=[]

one.append(row[0])

one.append(row[1])

one.append(row[2])

one.append(row[3])

one.append(row[4])

one.append(row[5])

one.append(row[6])

one.append(row[7])

one.append(row[8])

one.append(row[9])

one.append(row[10])

one.append(row[11])

one.append(row[12])

one.append(row[13])

one.append(row[14])

prescriptionData.append(one)

prescriptionDataFrame=pd.DataFrame(prescriptionData)

prescriptionDataSum= prescriptionDataFrame.sum(axis=0)

prescriptionDataSumSorted= sorted(enumerate(prescriptionDataSum), key=lambda x: x[1], reverse=True)

prescriptionDataSumSortedThirst= prescriptionDataSumSorted[:3]

prescriptionIndex=[]for i inrange(len(prescriptionDataSumSortedThirst)):

prescriptionIndex.append(prescriptionDataSumSortedThirst[i][0])

sql= "select prescriptionInfo,health from prescriptionInfo where FAMILY='%s' and ill_name='%s'" %(

sujects, resultName)

cursor.execute(sql)

rows=cursor.fetchall()

prescriptionInfoData=[]

healthData=[]for row inrows:

prescriptionInfoData.append(row[0])

healthData.append(row[1])

prescriptionInfoResult=[]for i inrange(len(prescriptionIndex)):

prescriptionInfoResult.append(prescriptionInfoData[prescriptionIndex[i]])

healthResult=[]for i inrange(len(prescriptionIndex)):

healthResult.append(healthData[prescriptionIndex[i]])returnprescriptionInfoResult, healthResultdefgetSeeProject(subject):

sql= "select CHACKPRO from chackProject where FAMILY='%s'" %(subject)

cursor.execute(sql)

rows=cursor.fetchall()

seeproject=[]for row inrows:

seeproject.append(rows[0])returnseeproject[0][0]defgetDoctors(sujects):

sql= "select addraction,name,summary from doctors where family='%s'" %(sujects)

cursor.execute(sql)

rows=cursor.fetchall()

doctorInfo=[]for row inrows:

doctorInfo.append(row[0])

doctorInfo.append(row[1])

doctorInfo.append(row[2])print("主治专家姓名：" + doctorInfo[1])print("主治专家简介：" + doctorInfo[2])print("主治专家所在医院：" +doctorInfo[0])returndoctorInfodefgetPAH(province, administrative):

sql= "select hostitalname from hostitalAdrrest where province='%s' and administrative='%s'" %(

province, administrative)

cursor.execute(sql)

rows=cursor.fetchall()

hostitalName=[]for row inrows:

hostitalName.append(row[0])

yiyuaan= ""

for i inrange(len(hostitalName)):print(hostitalName[i])

yiyuaan+=hostitalName[i]returnyiyuaandefaddUser_SeeIll(userid, username, sex, age, province, area, bumen, ke, result, chufang, jianyi, yiyuaan, yisheng,

jianchaxiang):

sql= "insert into zhenduanjilutable (userid,username,sex,age,province,area,bumen,ke,result,chufang,jianyi,yiyuaan,yisheng,jianchaxiang) values (%d,'%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')" %(

userid, username, sex, age, province, area, bumen, ke, result, chufang, jianyi, yiyuaan, yisheng, jianchaxiang)

cursor.execute(sql)

conn.commit()if (cursor.rowcount == 1):print("新看病记录添加成功")else:print("新看病记录添加失败")defzhenduan_n(a1, a2, a3, a4, a5, userid, username, surgery, surgeryChest, province, administ):

answers, str_answers=getAnswer(a1, a2, a3, a4, a5)

useranswer= [answers] * 3dataset, labless=getdata(surgery, surgeryChest)

x_data= tf.placeholder("float32", [None, 5])

y_data= tf.placeholder("float32", [None, 3])

weight= tf.Variable(tf.ones([5, 3]))

bias= tf.Variable(tf.ones([3]))#使用softmax激活函数

y_model = tf.nn.softmax(tf.matmul(x_data, weight) +bias)#y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

#loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

#使用交叉熵作为损失函数

loss = -tf.reduce_sum(y_data *tf.log(y_model))#train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

#使用AdamOptimizer优化器

#train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

train_step = tf.train.MomentumOptimizer(1e-4, 0.9).minimize(loss)#评估模型

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy= tf.reduce_mean(tf.cast(correct_prediction, "float"))

init=tf.initialize_all_variables()

sess=tf.Session()

sess.run(init)for _ in range(10):for i in range(int(len(dataset) / 100)):

sess.run(train_step, feed_dict={x_data: dataset[i:i + 100, :], y_data: labless[i:i + 100, :]})

xl_weight=sess.run(weight)

W=np.dot(xl_weight, useranswer)

result=0for i inrange(len(W[0])):for j inrange(len(W[0, :])):if (i ==j):

result+=W[i][j]

result= int(result / 5)if (result <= 3):

result= 3

elif (result <= 6):

result= 6

else:

result= 9sql= "select ILL_NAME from ill_result_tbZ where FAMILY='%s' and ILL_ID=%d" %(surgeryChest, result)

cursor.execute(sql)

rows=cursor.fetchall()

ILL_NAME=[]for row inrows:

ILL_NAME.append(row[0])

firstResult=ILL_NAME[0]if (firstResult[:2] == "疑似"):

firstResult= "疑似患病"

print(firstResult)

prescriptionInfoResult, healthResult=getSugessiones(surgeryChest, firstResult)

chufang= ''

for i inrange(len(prescriptionInfoResult)):

chufang= chufang + '诊断处方' + str(i + 1) + '：' + prescriptionInfoResult[i] + '。'

print('诊断处方' + str(i + 1) + '：' +prescriptionInfoResult[i])

jianyi= ''

for i inrange(len(healthResult)):

jianyi= jianyi + '养生建议' + str(i + 1) + '：' + healthResult[i] + '。'

print('养生建议' + str(i + 1) + '：' +healthResult[i])if (firstResult != '正常'):print("根据你的情况，系统推荐重点检查身体以下几项健康指标：")

seeproject=getSeeProject(surgeryChest)print("推荐检查：", seeproject)print("根据你的情况，系统向你推荐以下在这一治疗领域专家：")

doctorInfo=getDoctors(surgeryChest)

yisheng= ""

for i inrange(len(doctorInfo)):

yisheng+=doctorInfo[i]print("===============================================================================================")print("根据你所处的位置，系统找到以下与你距离最近的医院：")

yiyuaan=getPAH(province, administ)

sex= "男"age= 13addUser_SeeIll(userid, username, sex, age, province, administ, surgery, surgeryChest, firstResult, chufang,

jianyi, yiyuaan, yisheng, seeproject)#zhenduan_n("1","2","3","4","4",6,"小东东","外科","胸外科","广东省","广州市")#prescriptionInfoResult,healthResult = getSugessiones("胸外科","胸壁结核")#a = getDoctors("胸外科")#getPAH("广东省","广州市")

defaction(infon):

zhenduan_n(int(infon[0]),int(infon[1]),int(infon[2]),int(infon[3]),int(infon[4]),int(infon[5]),infon[6],infon[7],infon[8],infon[9],infon[10])if __name__ == '__main__':

infon=[]for i in range(1,len(sys.argv)):

infon.append(sys.argv[i])

action(infon)